Download Drugedrug interaction through molecular structure similarity analysis

Research and applications
Drugedrug interaction through molecular structure
similarity analysis
Santiago Vilar,1,2 Rave Harpaz,1 Eugenio Uriarte,2 Lourdes Santana,2 Raul Rabadan,1
Carol Friedman1
< Additional tables are
published online only. To view
these files please visit the
journal online (http://dx.doi.org/
10.1136/amiajnl-2012-000935).
1
Department of Biomedical
Informatics, Columbia University
Medical Center, New York, USA
2
Department of Organic
Chemistry, University of
Santiago de Compostela,
Santiago de Compostela, Spain
Correspondence to
Dr Santiago Vilar, Department of
Biomedical Informatics,
Columbia University Medical
Center, 622 West 168th St.
VC5, New York, NY 10032,
USA; [email protected]
Received 5 March 2012
Accepted 22 April 2012
Published Online First
30 May 2012
ABSTRACT
Background Drugedrug interactions (DDIs) are
responsible for many serious adverse events; their
detection is crucial for patient safety but is very
challenging. Currently, the US Food and Drug
Administration and pharmaceutical companies are
showing great interest in the development of improved
tools for identifying DDIs.
Methods We present a new methodology applicable on
a large scale that identifies novel DDIs based on
molecular structural similarity to drugs involved in
established DDIs. The underlying assumption is that if
drug A and drug B interact to produce a specific
biological effect, then drugs similar to drug A (or drug B)
are likely to interact with drug B (or drug A) to produce
the same effect. DrugBank was used as a resource for
collecting 9454 established DDIs. The structural similarity
of all pairs of drugs in DrugBank was computed to
identify DDI candidates.
Results The methodology was evaluated using as a gold
standard the interactions retrieved from the initial
DrugBank database. Results demonstrated an overall
sensitivity of 0.68, specificity of 0.96, and precision of
0.26. Additionally, the methodology was also evaluated
in an independent test using the Micromedex/Drugdex
database.
Conclusion The proposed methodology is simple,
efficient, allows the investigation of large numbers of
drugs, and helps highlight the etiology of DDI. A
database of 58 403 predicted DDIs with structural
evidence is provided as an open resource for
investigators seeking to analyze DDIs.
INTRODUCTION
Adverse drug events are a serious problem worldwide. In the US, they result in many injuries and
deaths each year,1e3 costing millions of dollars per
hospital annually and billions overall,4 5 and lead to
increased hospital care.6e8 Drugedrug interactions
(DDIs) are an important patient safety problem
and have been reported to cause up to 30% of
patient adverse events9 10 resulting in warning
notices for or the withdrawal of many drugs from
the market. The safety and efficacy profile of a drug
can be altered significantly by the co-administration of other drugs. Multiple drug combinations
in therapy are common11 and increase the risk of
adverse events since concomitant drugs can
share pharmacological or metabolic pathways. In
extreme cases, some drugs have caused death due to
the heightened adverse effect of the drug affected
by the interaction. For example, cerivastatin, a drug
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withdrawn from the US market, caused 31 cases of
fatal rhabdomyolysis prior to June 2001; the combination cerivastatinegemfibrozil was implicated in 12
of the 31 deaths.12 Gemfibrozil causes increased
blood levels of the statin resulting in a higher risk of
myopathy and rhabdomyolysis.
The development of tools to predict DDIs is
important in the drug development process and in
postmarketing surveillance in order to detect new
drug combinations that should be contraindicated.
Indeed, there is currently strong interest among
regulatory authorities, such as the US Food
and Drug Administration,13 and pharmaceutical
companies in developing better tools for assessing
drug interactions.14
The concept that similar molecules result in
similar biological properties has been employed
over the years by medicinal chemists.15e18 Methodologies such as QSAR/QSPR (Quantitative
Structure-Activity Relationship or Quantitative
Structure-Property Relationship), frequently used
in computer aided drug design, are very helpful to
establish relationships between the structures of
molecules and their corresponding biological
activity or other biological properties.16 19 Molecular fingerprint-based modeling has also been
applied successfully to the identification of molecules structurally similar to those with a selected
property.20 21 The same idea can be expanded to
explain DDIs based on their structural similarities.
In previous work, the similarity concept was used
to develop interesting approaches comparing biological targets through the chemical similarity of
their ligands.22 23
In this article we present a large-scale method for
DDI discovery and prediction that uses molecular
structure similarity information derived from
fingerprint-based modeling. Identifying new DDIs
using structural similarity is based on the basic idea
that if drug A interacts with drug B, and drug C is
structurally similar to A, then C should also
interact with B (the argument also follows if A is
replaced with B). Hence, by combining knowledge
of known interactions with structural similarity it
is possible to identify new interactions. As an
example, it has been reported in the medical literature24 and in Micromedex25 that simvastatin,
a drug that reduces levels of cholesterol by inhibiting the enzyme HMG-CoA reductase, can interact
with fluconazole, a triazole antifungal drug, resulting
in increased risk of myopathy or rhabdomyolysis. The
methodology presented in this paper suggests new
interactions by exploiting the concept that drugs
similar to simvastatin can also interact with fluconazole and cause a similar effect as described above.
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At the same time, drugs similar to fluconazole can interact with
simvastatin causing the same mentioned effect (figure 1 illustrates this with another example). We have created a database of
58 403 new predicted interactions (not mentioned in DrugBank)
for approved and experimental drugs, and have made this data
resource publically available (see online supplementary tables
S1eS3), which can be used by itself or in combination with
other methods to identify possible candidates and improve DDI
detection.
METHODS
DrugBank database
A total of 6624 drugs and 9454 DDIs mentioned in DrugBank
V.3.0 were used in this work.26 Drugs with more than one active
ingredient, such as oxtriphylline, aminophylline, or colesevelam,
and proteins and peptidic drugs were not included because
molecular fingerprints are not appropriate descriptors for these
types of molecules.
DrugBank DDI database
Drugs included in the DrugBank database were searched for
possible interactions using the Interax Interaction Search engine
on the DrugBank website,26 27 and duplicate DDIs from the
database were eliminated. Interaction information was available
for 928 drugs, resulting in a set of 9454 unique DDIs represented
as follows: drug A, the description of the effect, and drug B, as
shown in figure 1. The effect of the interaction associated with
drug pairs was included in our analysis (eg, the DrugBank entry
for the DDI tramadolenefazodone is: increased risk of serotonin
syndrome). To prepare for the calculation of DDI detection, the
spreadsheet with the set of known DDIs was then transformed
into a binary matrix M1 (with 928 rows and 928 columns)
where a matrix cell value of 1 represented a known interaction
Figure 1 Overview of the
construction of an interaction similarity
model. Employing a list of known
drugedrug interactions from DrugBank
(step 1), structural similarity
computation was carried out using
molecular fingerprints (step 2) and
a new list of predicted interactions
based on structural similarity was
generated (step 3).
between a pair of drugs and a value of 0 represented no
interaction.
Molecular structure similarity analysis
Structural similarity was identified in three steps:
1. Collecting and processing drug structures: Information on the
structures of the compounds in DrugBank was downloaded
from the website along with the SMILE code (a chemical
notation representing a chemical structure in linear textual
form). The molecular structures were preprocessed using the
Wash module implemented in MOE software,28 disconnecting group I metals in simple salts and retaining only
the largest molecular fragment. The protonation state was
considered neutral and explicit hydrogens were added. This
step is a common process necessary to prepare the molecules
for the next modeling process.
2. Structural representation: BIT_MACCS (MACCS Structural
Keys Bit packed) fingerprints were calculated for all molecules
included in the study.28 29 Different molecular fingerprints
have been published but the basic technique is to represent
a molecule as a bit vector that codes the presence or absence
of structural features where each feature is assigned a specific
bit position. For example, some structural features in the
BIT_MACCS fingerprint for the molecule C6H5-C(O)-NH2
are: bit 84 (NH2, amine group), bit 154 (C¼O, carbonyl
group), bit 162 (aromatic, C6H5), and bit 163 (six member
ring, C6H5).28 29
3. Similarity measures, computation, and data representation:
Different measures are used to compare similarity between
two molecular fingerprints. In this study, the molecular
fingerprints were compared using the widely applied Tanimoto coefficient (TC).29 30 The TC can span values between
0 and 1, where 0 means ‘maximum dissimilarity’ and 1 means
‘maximum similarity.’ The TC between two fingerprint
Drug A
Tramadol
Drug B
Increased risk of serotonin syndrome
Nefazodone
Step 1: established interactions
Step 2: structural similarity computation
Step 3: predicted interactions
Similar drug
To drug B
Drug A
Tramadol
Increased risk of serotonin syndrome
Similar drug
To drug A
Venlafaxine
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Trazodone
Drug B
Increased risk of serotonin syndrome
Nefazodone
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Research and applications
representations A and B is defined as the number of features
present in the intersection of both fingerprints A and B
divided by the number of features present in the union of
both fingerprints:
TCðA; BÞ ¼ j AXBj=j AWBj
Using the Fingerprint Cluster module and the sim_matrix2txt.svl script in MOE,28 a similarity matrix M2 was
constructed to capture the TC measure of similarity between
pairs of drugs in DrugBank (the matrix cell value represented the
TC between pairs of drugs).
Predicting new DDIs
From a technical standpoint, efficiently predicting all new DDIs
reduces to matrix multiplication of the matrices M1, which
consists of the established interactions, and M2, which consists
of the similarity matrix (see step 3a in figure 2). Values in the
diagonals of all the matrices are 0 because the interaction of
a drug with itself is not considered. However, the same interaction can be generated at different times based on similarities
obtained from different pairs, and therefore only the maximum
value in the array is retained for each entry, so that the predicted
interaction with the highest TC value only is considered (step 3b
in figure 2). As an example, the predicted interaction voriconazolee
triazolam, which increases the effect of the benzodiazepine, can
be generated from the interaction voriconazoleealprazolam (the
TC between triazolam and alprazolam is 0.98) or from the interaction voriconazoleemidazolam (the TC between triazolam and
midazolam is 0.91). In this case, the interaction associated with
the highest TC value is used, and the prevailing source for the
interaction voriconazoleetriazolam is the interaction voriconazolee
alprazolam. A symmetric transformation is carried out to obtain
the final M3 matrix (step 3c in figure 2), considering the highest
value for each pair of drugs (note that the matrix in 3b of figure 2
is not a symmetric matrix). In the example of figure 2, interactions 1e2 and 2e3 from M1 are retrieved in M3 with a TC>0.75.
Interaction 1e4 is retrieved by the model with a low score
(TC¼0.3). The model also predicts the new interaction 3e4
(TC¼0.9).
Once the final list of possible interactions is generated from
M3, the interactions are associated with the corresponding row
in the initial spreadsheet containing the effect of the interaction
so that the effect of the interaction can also be captured. The list
of interactions predicted by the model with TC>0.75 is given in
online supplementary table S1 for the initial 928 DrugBank
drugs used to construct the model. The same methodology was
applied to the other drugs in DrugBank for which no interaction
information was found in the Interax Interaction Search
module,27 generating a database of new interactions for 5696
approved, nutraceutical, and experimental drugs (see online
supplementary tables S2 and S3).
Evaluation
The performance of the model was evaluated by comparing the
predicted interactions based on our methodology when using
different TC cut-off values with the established interactions in
the initial DrugBank database. The interactions in the DrugBank
database were retrieved by the method based on maximum
similarity with other drug interaction pairs. The overall performance is summarized using the measures of sensitivity, specificity, precision, and enrichment factors. A receiver operating
characteristic (ROC) curve has been generated for more accurate
interpretation of model performance. A second evaluation by an
external source other than DrugBank was also carried out for the
Figure 2 Generating a drugedrug
interactions (DDI) similarity model
through combination of the DrugBank
interaction database and molecular
fingerprint-based modeling. In step 1,
interaction matrix M1 is created where
the interactions in DrugBank are
represented as ‘1’. In step 2, the
similarity matrix M2 is created based on
the Tanimoto coefficient values. In step
3, M13M2 is performed, the maximum
value for each entry is retained, and the
final matrix, M3, is formed based on
a symmetry-based transformation
(retrieved interactions from M1 when
TC>0.75 are represented in red, new
predicted interactions with TC>0.75
are represented in blue, and nonretrieved interactions from M1 when
TC>0.75 are represented in green).
Values in the diagonal of all the
matrices are 0 because the interaction
of a drug with itself is not considered.
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Research and applications
50 most frequently sold drugs in 2009,31 and the performance of
the method was assessed using Micromedex/Drugdex databases
as a gold standard to establish the number of correct predictions.
RESULTS
Analysis of model performance using the DrugBank database
A total of 9454 DDIs were obtained from DrugBank which were
associated with 928 drugs. Similarity information using molecular fingerprint-based modeling was computed for all 928 drugs
and integrated into the system as described in the Methods
section to develop the final model. Different cut-offs of similarity values of the TC were used to estimate sensitivity, specificity, precision, and an enrichment factor for the model. Based
on a TC>0.85, the model detected 4335 of the 9454 known
interactions in the DrugBank database. It was highly unlikely
that our system identified this set of 4335 interactions by chance
(p<0.0001, one-sided Fisher’s exact test). A random methodology considering the same number of possible cases (430 128
possible interactions) and the same number of true positive
cases (4335) and false positive cases (6792) as predicted by our
model, is capable of selecting only 245 known interactions (true
positives), whereas our method identified over 17-fold more
interactions. Table 1 shows the performance of our model
using different cut-off values for the TC. An ROC curve
containing all the possible interactions generated by the model
has been plotted in figure 3 and shows an area under the curve
of 0.92.
A sensitivity analysis through cross-validation was carried out
by dividing the database randomly into two sets: a training set
and a test set. Three evaluations were performed by moving
15%, 30%, and 45%, respectively, of the initial interactions to
the test set, and by constructing the model with the remaining
DrugBank interactions. Sensitivity and specificity values
were calculated for the three training and test sets and showed
metrics very close to the initial results using TC>0.75 (sensitivity was 0.64, 0.61, and 0.55; specificity was 0.96, 0.97, and
0.97 for the three models, respectively; see online supplementary
table S4 for more details). The robustness and the stability
of the final model were barely affected by the division of
two sets.
Table 1 Comparison of model performance on the DrugBank database
using different cut-off values for the Tanimoto coefficient
Model performance on the DrugBank database
Tanimoto coefficient >0.85
TP
FP
Total (TP+FP)
4335
6792
11127
FN
TN
Total (FN+TN)
5119
413882
419001
Tanimoto coefficient >0.80
5560
10018
15578
FN
TN
Total (FN+TN)
3894
410656
414550
Tanimoto coefficient >0.75
6442
18366
24808
FN
TN
Total (FN+TN)
3012
402308
405320
Sensitivity
0.46
Specificity
0.98
Precision
0.39
Enrichment factor
17.73
0.59
Specificity
0.98
0.36
Enrichment factor
16.24
0.68
Specificity
0.96
0.26
Enrichment factor
11.81
FN, false negatives; FP, false positives; Precision, TP/(TP+FP); Sensitivity, TP/(TP+FN);
Specificity, TN/(TN+FP); TN, true negatives; TP, true positives.
N retrieved known interactions in top x%
N interactions in top x%
Enrichment Factor (x%) ¼
N known interactions in DrugBank
N of possible interactions
J Am Med Inform Assoc 2012;19:1066–1074. doi:10.1136/amiajnl-2012-000935
Figure 3 Receiver operating characteristic (ROC) curve showing the
performance of the interaction model on the DrugBank database
(430 128 possible interactions were computed). The area under the
curve is 0.92.
Prediction of the effect produced by the DDI
Another feature of the model is its ability to detect the biological
effect produced by the DDI. As an example, an interaction could
produce an effect based on alterations in the bioavailability of
one of the drugs due to both drugs being metabolized by the
same enzymes or due to competition for the same transporter
protein. In order to verify whether the model is also capable of
predicting the effect produced by the DDI, a random selection
of DrugBank interactions was reviewed manually to determine
the degree of precision of the predicted biological effect. Out of
100 interactions selected using a TC cut-off value of 0.85, the
effect produced by the drug combination was correctly predicted
in 99 interactions where the effect was the same as that originally specified in DrugBank. Using other cut-offs, that is,
0.85$TC>0.80 and 0.80$TC>0.75, the model correctly
predicted the effect in 96% and 91% of the evaluated interactions, respectively (see online supplementary table S5 for more
details). However, in future predictions the nature of the
predicted interactions should be carefully analyzed, especially
when the TC is lower and the pharmacological class of the drugs
detected as structurally similar is different. For this reason, for
values of TC<0.85, appropriate pharmacological knowledge to
correctly interpret the effect of the interaction predicted would
be beneficial.
Evaluation in Micromedex/Drugdex
In the second part of the evaluation, interactions for the 50 most
frequent commercial drugs (consisting of 44 unique generic
drugs) sold in 2009 were searched in the Micromedex/Drugdex
database. Table 2 provides details of the sources of the drug
information as well as the results (see also figure 4). Specifically,
table 2A gives the number of interactions specified in DrugBank
and in Micromedex/Drugdex; table 2B gives the number of
predicted interactions and the number of interactions correctly
predicted by our model; and table 2C gives the sensitivity,
specificity, precision, and enrichment factor for the three
different TC cut-off values. A total of 1760 interactions were
associated with the drugs specified in Micromedex, and the
model predicted 548 interactions with a TC>0.75 (31% correct
classification) and 348 interactions with a TC>0.85 (20% correct
classification). Detailed results are given in table 2 and online
supplementary tables S6 and S7. It was highly unlikely that our
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Research and applications
A
Interactions described in DrugBank
Interactions described in Micromedex/Drugdex
Interactions described in Micromedex with
DrugBank drugs*
773
2323
1760
B
>0.85
1141
348
>0.80
1591
444
>0.75
2565
548
212
252
269
136
192
279
Sensitivity
0.20
Specificity
1412
61379
62791
Model performance (Tanimoto coefficient >0.80)
TP
FP
Total (TP+FP)
444
1147
1591
FN
TN
Total (FN+TN)
0.99
1316
61025
62341
Model performance (Tanimoto coefficient >0.75)
TP
FP
Total (TP+FP)
548
2017
2565
FN
TN
Total (FN+TN)
0.98
1212
0.97
60155
61367
Sensitivity
0.25
Specificity
Sensitivity
0.31
Specificity
Precision
0.30
Enrichment
factor
11.08
70
60
50
40
30
20
10
Sitagliptin
Naloxone
Pregabalin
Methylphenidate
Donepezil
Fluticasone
Celecoxib
Tadalafil
Aripiprazole
Losartan
Venlafaxine
Levothyroxine
Salmeterol
Duloxetine
Lansoprazole
Drospirenone
Hydrochlorothiazide
Albuterol
Oxycodone
Eszopiclone
Valacyclovir
Most frequently sold drugs in 2009
Precision
0.28
Enrichment
factor
10.14
Figure 4 Percentage of correct classifications for a random set of
interactions described in Micromedex/Drugdex for the 50 most frequently
sold drugs in 2009 (44 generic names) using the similarity interaction model
(TC>0.75) and a random set of drug interactions (more details are provided
in online supplementary tables S6 and S7). Drugs are sorted according to
the percentage classified correctly by the model. Only interactions
described in Micromedex/Drugdex but not in the DrugBank database are
taken into account. % Correct classification (Micromedex/Drugdex
interactions not described previously in DrugBank and correctly predicted
by the model). % Correct classification (random set of interactions).
Precision
0.21
Enrichment
factor
7.76
from ‘the existence of the interaction was clearly established
through controlled studies’ to ‘limited documentation but
pharmacological knowledge lead clinicians to recognize the
possible interaction’.
*The evaluation is carried out using the interactions described in Micromedex/Drugdex
when the drugs implicated in the interaction are also present in DrugBank and the molecular
structure is also available so the likelihood of the interaction can be computed.
yPredicted interactions using the 1454 drugs from DrugBank (approved and nutraceuticals).
FN, false negatives; FP, false positives; Precision, TP/(TP+FP); Sensitivity, TP/(TP+FN);
Specificity, TN/(TN+FP); TN, true negatives; TP, true positives.
model identified 348 true interactions by chance (p<0.0001, onesided Fisher’s exact test). A random method considering 63 932
possible interactions (interactions generated between 1454 drugs
from DrugBank and the 44 most frequently sold drugs in 2009)
and randomly selecting 1141 positive cases (the same as the
model when TC>0.85) would detect 31 interactions described in
the Micromedex database (1.78% correct classification).
The results identify interesting drug interactions belonging to
two categories. The nature of the system permits the identification of drugs belonging to pharmacological classes different
from those of the drugs implicated in the interaction (eg, drug A
and a similar drug C do not belong to the same pharmacological
class but each interact with drug B), which occurs more
frequently as the TC value decreases. However, the method is
more likely to identify interactions between drugs with similar
pharmacological profiles. The information provided by the
model in this case is more obvious but still could be very useful
to researchers, particularly those without a strong background in
pharmacology.
The interaction examples shown below were predicted by our
model and not described in DrugBank, but were described in
Micromedex/Drugdex with different levels of documentation,
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80
0
C
Model performance (Tanimoto coefficient >0.85)
TP
FP
Total (TP+FP)
348
793
1141
FN
TN
Total (FN+TN)
90
Mometasone
Tanimoto coefficient
Interactions predicted by the modely
Number of correct interactions predicted
by the model
Number of correct interactions predicted
by the model and described in DrugBank
Number of correct interactions predicted
by the model and not described in DrugBank
(ie, new for DrugBank)
100
% Correct classification
Table 2 Model performance predicting the interactions for the 50 most
frequently sold drugs in 2009 based on the Micromedex/Drugdex
database as a gold standard and using different cut-off values for the
Tanimoto coefficient
Examples of different pharmacological classes
Several interaction examples predicted for the 50 most
frequently sold drugs in 2009 showed that the DDI similarity
model can detect drugs that belong to different pharmacological
classes but have similar structural features (see table 3). An
example of an interaction correctly predicted by the model
according to the Micromedex/Drugdex database is aripiprazolee
nefazodone. Concomitant use of these drugs can cause increased
concentration of aripiprazole. Our model detected this interaction because the interaction aripiprazoleeitraconazole is described
in DrugBank, where the result is that itraconazole increases
the effect of aripiprazole. According to our analysis, itraconazole
shows some structural features similar to nefazodone (TC¼0.82),
although both molecules have different pharmacological profiles
(itraconazole is an antifungal and nefazodone is an antidepressant).
Another example of an interaction found by our methodology is
mometasone and different protease inhibitors used in HIV therapy,
such as indinavir, nelfinavir, ritonavir, and saquinavir, possibly
increasing the effect and toxicity of mometasone (see table 3).
Mometasone is similar to fusidic acid (TC¼0.77), and it is established in DrugBank that fusidic acid can interact with protease
inhibitors. The possible interaction mometasoneeprotease inhibitors is described in Micomedex/Drugdex and may cause increased
mometasone plasma concentrations due to inhibition of CYP3A4mediated mometasone metabolism by the antiretroviral drugs.
Buprenorphine, an opioid analgesic, has been found to share
some structural similarity with vinblastine, an antineoplastic
agent used for the treatment of different types of cancer
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Research and applications
Table 3 Examples of new predictions for the top 50 drugs not described in DrugBank but detected by our method and confirmed through
Micromedex/Drugdex
(TC¼0.76). The model correctly predicts, based on Micromedex/
Drugdex, that buprenorphine can interact with different protease
inhibitors (atazanavir, darunavir, indinavir, ritonavir, and saquinavir), with the antifungal ketoconazole and with the macrolide
antibiotic erythromycin, causing decreased metabolism of buprenorphine and increased drug plasma concentrations (see table 3).
Different interactions predicted by our model and described in
Micromedex/Drugdex have been found for venlafaxine, an antidepressant of the serotoninenorepinephrine reuptake inhibitor
(SNRI) class. According to our fingerprint-based model, tramadol
was found to be similar to venlafaxine with TC¼0.93. Therefore,
venlafaxine was predicted to interact with cimetidine, clozapine,
haloperidol, and dextroamphetamine, producing different plasma
concentrations of the drugs implicated in the interaction
(see table 3).
The possibility of finding drugs belonging to different classes
increases as the TC value decreases, which is interesting.
J Am Med Inform Assoc 2012;19:1066–1074. doi:10.1136/amiajnl-2012-000935
However, since the similarity is lower, the risk of incorrect
predictions is higher. For this reason, we considered a cut-off
value of 0.75 for the TC appropriate since similarity is still
remarkable and many different classes of related drugs can be
identified.
Examples of the same pharmacological classes
Although the DDI model can associate drugs which have
different pharmacological profiles but are structurally similar,
some of the predicted interactions can identify a drug belonging
to the same pharmacological class of one of the drugs implicated
in the known interaction. The DrugBank database describes
the interactions acetophenazineecisapride and acetophenazinee
terfenadine as resulting in an increased risk of cardiotoxicity and
arrhythmias. Our model detects that acetophenazine, a first
generation antipsychotic of the phenothiazine class, is similar to
quetiapine, a second generation antipsychotic, with TC¼0.78.
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Table 3
Continued
TC is the Tanimoto coefficient that measures similarity between two drugs.
Quetiapine is predicted to have the same interactions, which
were confirmed in Micromedex/Drugdex. Other examples of
predictions validated in Micromedex are the reduction in
hydrochlorotiazide absorption due to concomitant use of colestipol,
fenofibrate may increase the anticoagulant effect of phenprocoumon with risk of excessive bleeding, and buprenorphine can
interact with different opioids resulting in precipitation of
withdrawal symptoms (see table 3 and online supplementary
tables for more details).
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DISCUSSION
Different types of models for predicting DDIs have been recently
published.9 32e34 However, the majority of the in silico
approaches to predicting drug interactions have focused on the
integration of in vitro data to generate models for the in vivo
prediction of drug interactions.33 These models mainly try to
predict possible metabolic interactions, especially interactions
related to CYP enzymes. Nevertheless, there are many examples
of drugs that follow other metabolic routes. There are also many
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Research and applications
DDIs due to similar distribution profiles of the investigated
drugs. The importance of some mechanisms, as interaction with
transporters, has been recognized later.14
We propose a large-scale method based on identifying molecular similarity to analyze multiple types of drug interactions
caused by the inhibition of metabolizing enzymes, transporters,
or even the pharmacological targets. The model described in this
article can exploit experimental knowledge to identify the
possible causes of the interaction. The system allows the
researcher to monitor the data and the model’s predictions
preserve the nature of the original DDI that generates the
outcome, which is very useful for examining the effect and the
type of interaction predicted. Indeed, we reviewed 300 randomly
selected interactions and have shown that the system can
predict the effect of the interactions in more than 90% of cases
when TC>0.75 (see online supplementary table S5).
The model potentiates a visible pattern in the DrugBank
database (similar drugs have similar interactions) by detecting
drugs similar to the drugs implicated in the interactions
described previously. Therefore, one limitation of this study is
that the performance of the model depends on the comprehensiveness of the information in the original interaction database.
This method was applied to the interactions and drugs only
specified in DrugBank, but the addition of other sources of
established DDIs, such as those mentioned in drug labels, could
be taken into account to generate the final model.
An additional issue is that 2D similarity fingerprints were
used, which have some limitations in describing the molecular
structure. The 3D structure is a very important component in
the interaction drugereceptor and is a better representation of
the molecules.35 36 However, although the information provided
by 2D methods is more limited than the 3D information, the 2D
methods still offer good results and are much simpler and require
less computational effort, avoiding important problems such as
the selection of bioactive conformations and the calculation and
superimposition of the 3D structure of all the drugs implicated
in the study. Different 2D molecular fingerprints could also be
used in the development of this type of model.37 Nevertheless, in
the current study, BIT_MACCS fingerprints were calculated
because they are simple and have offered good results for
recognizing similar molecules in large databases.21 38 39
Although the similarity model provides valuable information
associated with the initial interactions, a more reliable and
complex system could be implemented through the integration
of structural similarity measures and knowledge in pharmacological databases containing information about possible targets
and metabolizing or transporter enzymes. This method could
also be combined with other methodologies using different
types of information, such as the Food and Drug Administration’s Adverse Event Reporting System,40 which was created to
provide postmarketing drug safety information, or the use of
clinical data in electronic health records.41 An extensive database
of annotated possible drug interactions predicted by our model
for the drugs in DrugBank (approved and experimental drugs) is
provided in online supplementary tables S1eS3). This database
is a valuable source of information on drug interactions that is
available for download and can be used by itself or in combination with other methods to filter out possible candidates and
improve DDI detection.
Several DDIs highlighted by our methodology were not known
and consequently were considered false positives in our evaluation. However, it is possible that some of these drugs actually do
interact but have not yet been identified. Therefore, it is possible
that the false positive rate is lower than we estimated.
J Am Med Inform Assoc 2012;19:1066–1074. doi:10.1136/amiajnl-2012-000935
CONCLUSION
The results presented in this study demonstrate the usefulness
of the proposed drugedrug interaction methodology as a promising approach for in silico prediction of drug interactions and
their effects. The method described in this article is very simple,
efficient, applicable to large-scale investigation and helps highlight the etiology of DDIs (see table 3). In this study, the
application of structure similarity information to drug interaction knowledge as specified in DrugBank led to retrieval of the
majority of known interactions, showing a sensitivity of 0.68
when the specificity was 0.96. A set of interactions not described
in the literature but with strong supporting evidence according
to our model has been constructed for further analysis. Experimental drugs were also evaluated by the model and ranked
according to interaction probability. The database of 58 403 new
predicted DDIs provided in this study could be useful for further
study of possible candidates, and is available for download
(online supplementary tables S1eS3). This database could be
used as a powerful pharmacovigilance tool by itself or combined
with other methods, such as the Food and Drug Administration’s Adverse Event Reporting System or electronic health
records, to facilitate drug safety by selecting candidates with
a strong possibility of interacting in the human body.
Contributors SV and CF conceived and designed the study; SV, RH, EU, LS, RR, and
CF suggested data and analysis tools; SV performed and analyzed the data; and SV,
RH, EU, LS, RR, and CF wrote the paper.
Funding This work was supported by grants R01 LM010016 (CF), R01
LM010016-0S1 (CF), R01 LM010016-0S2 (CF), R01 LM008635 (CF), and
1R01LM010140-01 (RR) from the National Library of Medicine, ‘Plan Galego de
Investigación, Innovación e Crecemento 2011-2015 (I2C)’, the European Social Fund
(ESF), and the Angeles Alvariño program from Xunta de Galicia (Spain).
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Classen DC, Pestotnik SL, Evans RS, et al. Adverse drug events in hospitalized
patients. Excess length of stay, extra costs, and attributable mortality. JAMA
1997;277:301e6.
Cullen DJ, Sweitzer BJ, Bates DW, et al. Preventable adverse drug events in
hospitalized patients: a comparative study of intensive care and general care units.
Crit Care Med 1997;25:1289e97.
Cullen DJ, Bates DW, Small SD, et al. The incident reporting system does not
detect adverse drug events: a problem for quality improvement. Jt Comm J Qual
Improv 1995;21:541e8.
Bates DW, Spell N, Cullen DJ, et al. The costs of adverse drug events in hospitalized
patients. Adverse Drug Events Prevention Study Group. JAMA 1997;277:307e11.
Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential
adverse drug events-implications for prevention. ADE Prevention Study Group. JAMA
1995;274:29e34.
Mjorndal T, Boman MD, Hagg S, et al. Adverse drug reactions as a cause for
admissions to a department of internal medicine. Pharmacoepidemiol Drug Saf
2002;11:65e72.
Schneeweiss S, Hasford J, Gottler M, et al. Admissions caused by adverse drug
events to internal medicine and emergency departments in hospitals: a longitudinal
population-based study. Eur J Clin Pharmacol 2002;58:285e91.
Hohl CM, Nosyk B, Kuramoto L, et al. Outcomes of emergency department patients
presenting with adverse drug events. Ann Emerg Med 2011;58:270e9.e4.
Tatonetti NP, Fernald GH, Altman RB. A novel signal detection algorithm for
identifying hidden drug-drug interactions in adverse event reports. J Am Med Inform
Assoc. Published Online First: 2011. doi:10.1136/amiajnl-2011-000214
Pirmohamed M. Ml O: Drug Interactions of Clinical Importance. London: Chapman &
Hall, 1998.
Harpaz R, Chase HS, Friedman C. Mining multi-item drug adverse effect
associations in spontaneous reporting systems. BMC Bioinformatics 2010;11
(Suppl 9):S7.
Staffa JA, Chang J, Green L. Cerivastatin and reports of fatal rhabdomyolysis.
N Engl J Med 2002;346:539e40.
http://www.fda.gov/
Bjornsson TD, Callaghan JT, Einolf HJ, et al. The conduct of in vitro and in vivo drugdrug interaction studies: a Pharmaceutical Research and Manufacturers of America
(PhRMA) perspective. Drug Metab Dispos 2003;31:815e32.
1073
Research and applications
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
Martin YC, Kofron JL, Traphagen LM. Do structurally similar molecules have similar
biological activity? J Med Chem 2002;45:4350e8.
Vilar S, Santana L, Uriarte E. Probabilistic neural network model for the in silico
evaluation of anti-HIV activity and mechanism of action. J Med Chem 2006;49:1118e24.
Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating
environment (MOE): application of QSAR and molecular docking to drug discovery.
Curr Top Med Chem 2008;8:1555e72.
Gedeck P, Lewis RA. Exploiting QSAR models in lead optimization. Curr Opin Drug
Discov Devel 2008;11:569e75.
Winkler DA. The role of quantitative structureeactivity relationships (QSAR) in
biomolecular discovery. Brief Bioinform 2002;3:73e86.
Costanzi S, Vilar S, Micozzi D, et al. Delineation of the molecular mechanisms of
nucleoside recognition by cytidine deaminase through virtual screening. Chem Med
Chem 2011;6:1452e8.
Ewing T, Baber JC, Feher M. Novel 2D fingerprints for ligand-based virtual
screening. J Chem Inf Model 2006;46:2423e31.
Hert J, Keiser MJ, Irwin JJ, et al. Quantifying the relationships among drug classes.
J Chem Inf Model 2008;48:755e65.
Keiser MJ, Roth BL, Armbruster BN, et al. Relating protein pharmacology by ligand
chemistry. Nat Biotechnol 2007;25:197e206.
Molden E, Skovlund E, Braathen P. Risk management of simvastatin or atorvastatin
interactions with CYP3A4 inhibitors. Drug Saf 2008;31:587e96.
Micromedex Ò Healthcare Series [Internet database]. Greenwood Village,
Colo: Thomson Reuters (Healthcare) Inc. Thomson Reuters; USA. Updated
periodically. 2011.
DrugBank Database, Version 3.0. http://www.drugbank.ca/ (accessed Apr 2011).
http://www.drugbank.ca/interax/drug_lookup (accessed Apr 2011).
MOE, Version 2011.10; Chemical Computing Group, Inc. http://www.chemcomp.com
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
MACCS Keys; MDL Information Systems I, 14600 Catalina Street, San Leandro, CA
94577
http://www.daylight.com/dayhtml/doc/theory/theory.finger.html
http://www.drugs.com/top200_units.html
Dickins M, Van de Waterbeemd H. Simulation models for drug disposition and drug
interactions. Drug Discov Today Biosilico 2004;2:38e45.
Zhang L, Zhang YC, Zhao P, et al. Predicting drug-drug interactions: an FDA
perspective. AAPS J 2009;11:300e6.
Tatonetti NP, Denny JC, Murphy SN, et al. Detecting drug interactions from
adverse-event reports: interaction between paroxetine and pravastatin increases
blood glucose levels. Clin Pharmacol Ther 2011;90:133e42.
Vilar S, Karpiak J, Costanzi S. Ligand and structure-based models for the prediction
of ligand-receptor affinities and virtual screenings: development and application to
the beta(2)-adrenergic receptor. J Comput Chem 2010;31:707e20.
Engel S, Skoumbourdis AP, Childress J, et al. A virtual screen for diverse ligands:
discovery of selective G protein-coupled receptor antagonists. J Am Chem Soc
2008;130:5115e23.
Steffen A, Kogej T, Tyrchan C, et al. Comparison of molecular fingerprint methods on
the basis of biological profile data. J Chem Inf Model 2009;49:338e47.
Durant JL, Leland BA, Henry DR, et al. Reoptimization of MDL keys for use in drug
discovery. J Chem Inf Comput Sci 2002;42:1273e80.
Vilar S, Harpaz R, Chase HS, et al. Facilitating adverse drug event detection in
pharmacovigilance databases using molecular structure similarity: application to
rhabdomyolysis. J Am Med Inform Assoc 2011;18(Suppl 1):i73e80.
Adverse Event Reporting System. http://www.fda.gov/cder/aers/default.htm
Wang X, Hripcsak G, Markatou M, et al. Active computerized pharmacovigilance
using natural language processing, statistics, and electronic health records:
a feasibility study. J Am Med Inform Assoc 2009;16:328e37.
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